Xiaoming Fu

This paper develops a graph neural network method to predict spatio-temporal stochastic gene expression efficiently, achieving faster computation and extrapolation capabilities.

This paper introduces a method to simultaneously estimate changepoints and model parameters in switching dynamical systems, demonstrating its effectiveness through simulations and a real-world COVID-19 case study.

DelaySSAToolkit.jl provides a Julia package for simulating reaction systems with time delays using exact formulations of the delay stochastic simulation algorithm.

This paper uses neural networks to approximate non-Markov stochastic gene expression models and infer their parameters from noisy data.